Jenna Tomkinson

This study examines tissue-specific transcriptional responses to cold stress in Drosophila, finding distinct gene expression patterns across tissues.

The paper introduces CytoTable, a software package that harmonizes single-cell image-based profiling data from different tools, enabling reproducible and scalable data integration.

This paper uses high-content live-cell time-lapse imaging to predict apoptosis in HeLa cells using machine learning, detecting it 100 minutes after treatment with Staurosporine.

coSMicQC is a Python package for single-cell image-based profiling quality control, improving data quality by filtering low-quality cells and reducing technical noise.

The paper uses high-content microscopy and machine learning to identify a cell morphology signature of NF1 genotype in Schwann cells, but model performance declines when applied to new cell lines.

The paper characterizes pyroptosis using secretome profiling and morphology analysis, revealing complex connections with apoptosis through machine learning models.

This paper predicts single-cell phenotypes from nuclear morphology using pre-labeled microscopy data, achieving high accuracy for some phenotypes but facing generalizability challenges.